In wireless, local area networks (WLANs), a wireless device scans all available channels to detect nearby access points (APs) and then associates itself with an AP that has the strongest received signal strength indicator (RSSI) without taken into consideration the load on such an AP or on other nearby APs. Recent studies on operational IEEE 802.11 WLANs have shown that traffic loads are often unevenly distributed among APs within a WLAN. Typically, at any given point in time some APs tend to suffer from heavy loads (so-called “congested” APs) while others do not. This situation creates a load imbalance within a WLAN. Load imbalances are undesirable because they hamper a network from fully utilizing its capacity and prevent the network from providing services in a fair and even manner.
Currently the IEEE 802.11 WLAN standard does not provide a set method to resolve load imbalances. To overcome this deficiency, various load balancing schemes have been proposed by both academia and industry. Most of these methods take the approach of directly controlling user-AP associations by deploying proprietary client software or specially designed WLAN cards in devices (e.g., computers) operated by users. In these approaches APs broadcast their load levels to user devices (sometimes referred to as just “users”) via modified beacon messages, and each user chooses the least-loaded AP.
Although such a user selection approach can achieve load balancing, the deployment of proprietary client software/hardware to all (or most) user devices is difficult to achieve. For example, today users access a variety of WLANs such as hotels, airports, shopping centers and university campuses. These different networks are managed by different organizations that have most likely adopted different load balancing mechanisms. It is unrealistic to expect users to have multiple, different client modules; one for each different network.
Therefore, it is desirable to provide new load-balancing schemes that do not require the use of proprietary client modules and the like.
Other types of networks also face load-balancing challenges. For example, in CDMA cellular networks an increase in the number of active users in a cell causes an increase in the total interference sensed at the cell's base stations. This causes the cell to become congested. When a cell becomes congested, devices being operated by users within the cell need to transmit at higher power levels to overcome the effects of interference in order to ensure signals they are transmitting to base stations are received at acceptable signal-to-interference ratios. As power levels within a cell increase, the signals generated cause increased interference with neighboring cells. As a result, the overall capacity of a network containing such cells begins to decrease. To overcome these unwanted increases in interference, so-called “cell breathing” techniques have been developed. Generally speaking, however, existing cell-breathing techniques developed for CDMA networks do not work well in WLANs.
Because WLANs face similar load-balancing challenges as in CDMA networks the present inventors began to study how to solve these challenges by first realizing the shortcomings of existing cell breathing techniques. For example, referring to FIG. 1(a), there is shown a WLAN 1 with three AP's, a, b and c that are assumed to be transmitting at the same maximal power level. For the sake of simplicity, we will assign a number of users to each AP. In FIG. 1(a), 1 user is initially assigned to AP a, 8 to AP b, and 1 to AP c. In this example, we define the load of an AP to be the number of its assigned or associated users. Given this scenario, AP b has a much higher load than APs a and c. In accordance with an existing cell breathing technique, to reduce the load on AP b its transmission power must be reduced. This leads to a reduction in the transmission range/cell size of b. In FIG. 1(a) the range shrinks from boundary 101 to 102, for example. As is illustrated in FIG. 1(a), the four users/devices 1-4 located farthest away from AP b are affected by this reduction in cell size. At its original size, users 1-4 are within the cell and, therefore, within range of AP b transmissions. As the cell shrinks from size 101 to 102 the users 1-4 find themselves located at the very edge of AP b's range (and sometimes outside the range of course). Being farther away from AP b typically results in a decrease in signal quality at the devices used by users 1-4. In response to the detection of a lower signal quality, the devices used by users 1-4 initiate scanning operations to select an AP associated with a higher signal quality. For example, two of the users 1-2 in FIG. 1(a) may detect a higher signal quality from AP a, while users 3-4 detect a higher signal quality from AP c. Once a higher signal quality is detected, in accordance with an existing cell breathing technique, the users are shifted to APs a and c, respectively. As illustrated in FIG. 1(b), the net effect is to more evenly distribute the load/users within WLAN 1 (i.e., because 3 users are now assigned to AP a, 4 to AP b, and 3 to AP c).
However, a reduction in the transmission power of AP b affects the signal quality of the channels/links between AP b and all of the users within its cell, not just users 1-4. Thus, users/devices 5-8 that are not shifted to another AP (i.e., remain associated with AP b) also detect lower signal qualities. In response, the devices used by users 5-8 may have to communicate at a lower bit rate. At slower bit rates, it may take longer for information (sometimes referred to as “traffic”) to be transmitted from a user 5-8 to AP b. This effectively increases the load contributed by each user 5-8 on AP b (if the AP load is determined by considering not only the number of users but also effective user throughput). Thus, instead of reducing the load on an AP, existing cell breathing techniques may actually increase the load.
Accordingly, it is desirable to provide methods and devices that avoid or minimize load imbalances within wireless networks. In particular, it is desirable to provide more effective cell breathing methods (and associated devices) that can be used to minimize load imbalances within WLANs.